Early Detection of Rotor Faults in Three-Phase Induction Motors Using Predictive Monitoring

Predictive monitoring and early rotor fault diagnosis are necessary to improve the three-phase induction motors' dependability, durability, and operational efficiency. This study investigates a predictive monitoring system that collects critical characteristics such as temperature, speed, and vibration and integrates real-time sensor data to optimize motor performance. The gadget ensures continuous operation by minimizing mechanical loads and preventing early degradation through the use of adaptive rotor balancing techniques. Predictive maintenance driven by machine learning enables proactive problem detection, which lowers unplanned downtime and operational costs. Experimental results indicate that greater current ratings under high load conditions may cause more vibrations, which may affect the overall condition of the bearings and the operation of the motor. Motor stability is significantly impacted by unbalanced rotor components, and changes in vibration pattern and temperature provide crucial hints about potential issues. A comparative analysis highlights the importance of continuous monitoring in maintaining optimal conditions and preventing catastrophic failures. The findings show that in order to improve the reliability, sustainability, and cost-effectiveness of industrial applications, rotor balancing techniques must be combined with state-of-the-art diagnostic tools.